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Internet of things is characterized by limited energy and distributed topology. In this scenario, a secure and energy-saving routing algorithm plays a significant role in ensuring the performance of Internet of things. Due to different standards and communication stacks, traditional security mechanisms heavily rely on trust management to function well. In this article, we propose an energy-aware trustworthy multi-hop routing model for Internet of things. To balance energy consumption and trust, the whole routing from source to destination should be considered for trust appraisal, rather than the destination peers merely. Meanwhile, the energy consumption of wise interaction should be regarded as one criterion. We also introduce a trustworthy spanning tree algorithm that is able to balance energy consumption and reduce trust cost. The experimental results suggest that the proposed strategy can lower energy consumption significantly under the premise of trustworthy transmission.

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Integration of the IoT (Internet of Things) with Cloud Computing, termed as the CoT (Cloud of Things) can help achieve the goals of the envisioned IoT and future Internet. In a typical CoT infrastructure, the data collected from wireless sensor networks and IoTs is transmitted through a SG (Smart Gateway) to the cloud. The bandwidth between an IoT access point and SG becomes a bottleneck for information transmission between the IoT and the cloud. We propose a novel game theory model to describe the CoT attacker, who expects to use minimum set and energy consumption of IoT attack devices to occupy as many bandwidth resources as possible in a given time period; and the defender, who expects to minimize false alarms. By analyzing this model, we have found that the game theory model is a non-cooperative and repeated incomplete information game, and Nash equilibrium is existent, perfected by the subgame. The best strategy for each stage of the attack is to adjust the attack link number dynamically based on the comparison results of value and turning point for each time period. At the same time, the defender adjusts the threshold value ȕ dynamically, based on the comparison results of the Load YDOXH DQG H[SHFWHG YDOXH RI Į IRU HDFK WLPH SHULRG 7KH VLPXODWLRQ UHVXOW VKRZV WKDW RXU VWUDWHJ\ FDQ VLJQL¿FDQWO\ PLWLJDWH WKH harm of a distributed denial of service attack.

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